Tree diversity reduces risk of bark beetle infestation for preferred conifer species, but increases risk for less preferred hosts
Bibliographic record
Abstract
1. In recent decades, European temperate forests have repeatedly suffered from severe droughts. Drought-weakened forests have often become more susceptible to pest outbreaks such as bark-beetle infestations. Tree-species diversity is expected to increase resistance to drought and pests, but evidence for a positive tree diversity effect on insect pest reduction is largely circumstantial. 2. Here we tested the effects of tree diversity and biogeographic origin of tree species on bark-beetle infestation in a large, young tree-diversity experiment, with six broadleaved and six conifer species from Europe and North America. Lower infestation risk was expected for the exotic tree species in each congeneric pair (spruce, larch and pine) and for mixtures with higher species richness and higher broadleaf proportion. Following a severe drought in summer 2018, the conifer trees were attacked by the six-toothed spruce bark beetle (Pityogenes chalcographus). Bark-beetle boreholes were recorded in winter 2018/19 on all conifer species. 3. Norway spruce (Picea abies) and European larch (Larix decidua) were the most infested species and thus considered main hosts of the bark beetle. For these two species, probability of infestation decreased with increasing tree diversity (although this was only significant for Larix). In contrast, Pinus, which were less infested overall, were more likely to be infested in plots with high tree diversity. Exotic trees tended to be less infested, with clearest support for enemy release found at the level of infestation intensity when considering pure conifer stands. Overall, effects of tree diversity and tree-species origin were not as strong as the effect of position within the experimental site, where higher rates of infestation were observed at the edge than in the centre. 4. Synthesis. Increasing tree diversity may reduce the risk of bark-beetle infestation for genera prone to high infestation rates (Picea and Larix), but risk for less preferred genera (Pinus, and to some extent the exotic tree species) may increase with tree diversity due to spill-over from preferred hosts. In mixed forests, risk of infestation, even by relatively specialized insect pests, may be re-distributed among tree species rather than reduced for all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".